Overview

Dataset statistics

Number of variables37
Number of observations31
Missing cells238
Missing cells (%)20.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.9 KiB
Average record size in memory1.0 KiB

Variable types

Categorical16
Text2
Numeric14
DateTime5

Alerts

active_ingredient has constant value ""Constant
atc has constant value ""Constant
pack_strength has constant value ""Constant
price_participants_24 has constant value ""Constant
price_participants_4 has constant value ""Constant
price_participants_5 has constant value ""Constant
price_participants_8 has constant value ""Constant
sku has constant value ""Constant
contract_id is highly overall correlated with price_participants_16 and 2 other fieldsHigh correlation
contract_type is highly overall correlated with price_participants_6 and 1 other fieldsHigh correlation
duration is highly overall correlated with duration_total_new and 2 other fieldsHigh correlation
duration_ext is highly overall correlated with outcome and 2 other fieldsHigh correlation
duration_total_new is highly overall correlated with duration and 1 other fieldsHigh correlation
maximum_price_allowed is highly overall correlated with price_participants_16 and 7 other fieldsHigh correlation
outcome is highly overall correlated with duration_ext and 5 other fieldsHigh correlation
participants_no is highly overall correlated with price_participants_16 and 4 other fieldsHigh correlation
price_participants_16 is highly overall correlated with contract_id and 7 other fieldsHigh correlation
price_participants_19 is highly overall correlated with maximum_price_allowed and 2 other fieldsHigh correlation
price_participants_23 is highly overall correlated with maximum_price_allowed and 2 other fieldsHigh correlation
price_participants_6 is highly overall correlated with contract_type and 11 other fieldsHigh correlation
price_participants_7 is highly overall correlated with contract_id and 17 other fieldsHigh correlation
quantity_ext is highly overall correlated with price_participants_6 and 4 other fieldsHigh correlation
quantity_monthly is highly overall correlated with price_participants_6 and 4 other fieldsHigh correlation
quantity_total is highly overall correlated with price_participants_6 and 4 other fieldsHigh correlation
quantity_yearly is highly overall correlated with price_participants_6 and 4 other fieldsHigh correlation
second_place is highly overall correlated with maximum_price_allowed and 4 other fieldsHigh correlation
second_place_outcome is highly overall correlated with duration_ext and 7 other fieldsHigh correlation
second_place_price is highly overall correlated with maximum_price_allowed and 3 other fieldsHigh correlation
winner is highly overall correlated with contract_id and 5 other fieldsHigh correlation
winner_price is highly overall correlated with maximum_price_allowed and 4 other fieldsHigh correlation
outcome is highly imbalanced (79.4%)Imbalance
price_participants_16 has 9 (29.0%) missing valuesMissing
price_participants_19 has 17 (54.8%) missing valuesMissing
price_participants_23 has 19 (61.3%) missing valuesMissing
price_participants_24 has 30 (96.8%) missing valuesMissing
price_participants_4 has 30 (96.8%) missing valuesMissing
price_participants_5 has 30 (96.8%) missing valuesMissing
price_participants_6 has 28 (90.3%) missing valuesMissing
price_participants_7 has 29 (93.5%) missing valuesMissing
price_participants_8 has 30 (96.8%) missing valuesMissing
second_place_price has 16 (51.6%) missing valuesMissing
contract_id is uniformly distributedUniform
price_participants_7 is uniformly distributedUniform
contract_id has unique valuesUnique
published_date has unique valuesUnique
quantity_total has unique valuesUnique
quantity_monthly has unique valuesUnique
quantity_yearly has unique valuesUnique
quantity_ext has unique valuesUnique
duration_ext has 7 (22.6%) zerosZeros

Reproduction

Analysis started2024-05-05 12:16:09.480028
Analysis finished2024-05-05 12:16:39.108808
Duration29.63 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

active_ingredient
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
molecule_x
31 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters310
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmolecule_x
2nd rowmolecule_x
3rd rowmolecule_x
4th rowmolecule_x
5th rowmolecule_x

Common Values

ValueCountFrequency (%)
molecule_x 31
100.0%

Length

2024-05-05T13:16:39.351870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T13:16:39.502351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
molecule_x 31
100.0%

Most occurring characters

ValueCountFrequency (%)
l 62
20.0%
e 62
20.0%
m 31
10.0%
o 31
10.0%
c 31
10.0%
u 31
10.0%
_ 31
10.0%
x 31
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 310
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 62
20.0%
e 62
20.0%
m 31
10.0%
o 31
10.0%
c 31
10.0%
u 31
10.0%
_ 31
10.0%
x 31
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 310
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 62
20.0%
e 62
20.0%
m 31
10.0%
o 31
10.0%
c 31
10.0%
u 31
10.0%
_ 31
10.0%
x 31
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 310
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 62
20.0%
e 62
20.0%
m 31
10.0%
o 31
10.0%
c 31
10.0%
u 31
10.0%
_ 31
10.0%
x 31
10.0%

atc
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
C07AB07
31 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters217
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC07AB07
2nd rowC07AB07
3rd rowC07AB07
4th rowC07AB07
5th rowC07AB07

Common Values

ValueCountFrequency (%)
C07AB07 31
100.0%

Length

2024-05-05T13:16:39.660514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T13:16:39.804613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
c07ab07 31
100.0%

Most occurring characters

ValueCountFrequency (%)
0 62
28.6%
7 62
28.6%
C 31
14.3%
A 31
14.3%
B 31
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 217
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 62
28.6%
7 62
28.6%
C 31
14.3%
A 31
14.3%
B 31
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 217
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 62
28.6%
7 62
28.6%
C 31
14.3%
A 31
14.3%
B 31
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 217
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 62
28.6%
7 62
28.6%
C 31
14.3%
A 31
14.3%
B 31
14.3%

buyer
Text

Distinct19
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-05T13:16:40.022738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.483871
Min length7

Characters and Unicode

Total characters232
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)32.3%

Sample

1st rowbuyer_1
2nd rowbuyer_2
3rd rowbuyer_3
4th rowbuyer_4
5th rowbuyer_5
ValueCountFrequency (%)
buyer_8 3
 
9.7%
buyer_11 3
 
9.7%
buyer_2 3
 
9.7%
buyer_19 2
 
6.5%
buyer_3 2
 
6.5%
buyer_5 2
 
6.5%
buyer_7 2
 
6.5%
buyer_16 2
 
6.5%
buyer_12 2
 
6.5%
buyer_13 1
 
3.2%
Other values (9) 9
29.0%
2024-05-05T13:16:40.510564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
b 31
13.4%
u 31
13.4%
y 31
13.4%
e 31
13.4%
r 31
13.4%
_ 31
13.4%
1 19
8.2%
2 5
 
2.2%
8 4
 
1.7%
9 3
 
1.3%
Other values (6) 15
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 31
13.4%
u 31
13.4%
y 31
13.4%
e 31
13.4%
r 31
13.4%
_ 31
13.4%
1 19
8.2%
2 5
 
2.2%
8 4
 
1.7%
9 3
 
1.3%
Other values (6) 15
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 31
13.4%
u 31
13.4%
y 31
13.4%
e 31
13.4%
r 31
13.4%
_ 31
13.4%
1 19
8.2%
2 5
 
2.2%
8 4
 
1.7%
9 3
 
1.3%
Other values (6) 15
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 31
13.4%
u 31
13.4%
y 31
13.4%
e 31
13.4%
r 31
13.4%
_ 31
13.4%
1 19
8.2%
2 5
 
2.2%
8 4
 
1.7%
9 3
 
1.3%
Other values (6) 15
6.5%

contract_id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-05-05T13:16:40.722500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q18.5
median16
Q323.5
95-th percentile29.5
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0921211
Coefficient of variation (CV)0.56825757
Kurtosis-1.2
Mean16
Median Absolute Deviation (MAD)8
Skewness0
Sum496
Variance82.666667
MonotonicityStrictly increasing
2024-05-05T13:16:40.920676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1
 
3.2%
17 1
 
3.2%
30 1
 
3.2%
29 1
 
3.2%
28 1
 
3.2%
27 1
 
3.2%
26 1
 
3.2%
25 1
 
3.2%
24 1
 
3.2%
23 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
1 1
3.2%
2 1
3.2%
3 1
3.2%
4 1
3.2%
5 1
3.2%
6 1
3.2%
7 1
3.2%
8 1
3.2%
9 1
3.2%
10 1
3.2%
ValueCountFrequency (%)
31 1
3.2%
30 1
3.2%
29 1
3.2%
28 1
3.2%
27 1
3.2%
26 1
3.2%
25 1
3.2%
24 1
3.2%
23 1
3.2%
22 1
3.2%

contract_type
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
regional
24 
multi-region
wide area
 
1

Length

Max length12
Median length8
Mean length8.8064516
Min length8

Characters and Unicode

Total characters273
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st rowregional
2nd rowwide area
3rd rowregional
4th rowregional
5th rowregional

Common Values

ValueCountFrequency (%)
regional 24
77.4%
multi-region 6
 
19.4%
wide area 1
 
3.2%

Length

2024-05-05T13:16:41.141996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T13:16:41.317509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
regional 24
75.0%
multi-region 6
 
18.8%
wide 1
 
3.1%
area 1
 
3.1%

Most occurring characters

ValueCountFrequency (%)
i 37
13.6%
e 32
11.7%
r 31
11.4%
g 30
11.0%
o 30
11.0%
n 30
11.0%
l 30
11.0%
a 26
9.5%
m 6
 
2.2%
u 6
 
2.2%
Other values (5) 15
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 37
13.6%
e 32
11.7%
r 31
11.4%
g 30
11.0%
o 30
11.0%
n 30
11.0%
l 30
11.0%
a 26
9.5%
m 6
 
2.2%
u 6
 
2.2%
Other values (5) 15
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 37
13.6%
e 32
11.7%
r 31
11.4%
g 30
11.0%
o 30
11.0%
n 30
11.0%
l 30
11.0%
a 26
9.5%
m 6
 
2.2%
u 6
 
2.2%
Other values (5) 15
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 37
13.6%
e 32
11.7%
r 31
11.4%
g 30
11.0%
o 30
11.0%
n 30
11.0%
l 30
11.0%
a 26
9.5%
m 6
 
2.2%
u 6
 
2.2%
Other values (5) 15
5.5%

duration
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.419355
Minimum12
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-05-05T13:16:41.454675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12
Q124
median36
Q337.5
95-th percentile48
Maximum49
Range37
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation11.552126
Coefficient of variation (CV)0.34567172
Kurtosis-0.53357719
Mean33.419355
Median Absolute Deviation (MAD)12
Skewness-0.4979471
Sum1036
Variance133.45161
MonotonicityNot monotonic
2024-05-05T13:16:41.618273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
36 13
41.9%
48 6
19.4%
24 6
19.4%
12 4
 
12.9%
39 1
 
3.2%
49 1
 
3.2%
ValueCountFrequency (%)
12 4
 
12.9%
24 6
19.4%
36 13
41.9%
39 1
 
3.2%
48 6
19.4%
49 1
 
3.2%
ValueCountFrequency (%)
49 1
 
3.2%
48 6
19.4%
39 1
 
3.2%
36 13
41.9%
24 6
19.4%
12 4
 
12.9%
Distinct28
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Memory size380.0 B
Minimum2015-03-31 00:00:00
Maximum2027-03-30 00:00:00
2024-05-05T13:16:41.798698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:42.011694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)

maximum_price_allowed
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.088368841
Minimum1 × 10-5
Maximum0.99931561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-05-05T13:16:42.226857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-5
5-th percentile1 × 10-5
Q10.01225
median0.0242
Q30.06
95-th percentile0.47971424
Maximum0.99931561
Range0.99930561
Interquartile range (IQR)0.04775

Descriptive statistics

Standard deviation0.22771038
Coefficient of variation (CV)2.5768175
Kurtosis12.670157
Mean0.088368841
Median Absolute Deviation (MAD)0.01885
Skewness3.6761071
Sum2.7394341
Variance0.051852018
MonotonicityNot monotonic
2024-05-05T13:16:42.428491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 × 10-53
 
9.7%
0.03929 2
 
6.5%
0.07 2
 
6.5%
0.0004 1
 
3.2%
0.9993156096 1
 
3.2%
0.02679 1
 
3.2%
0.034 1
 
3.2%
0.01571 1
 
3.2%
0.0122 1
 
3.2%
0.018 1
 
3.2%
Other values (17) 17
54.8%
ValueCountFrequency (%)
1 × 10-53
9.7%
5 × 10-51
 
3.2%
0.0001 1
 
3.2%
0.0004 1
 
3.2%
0.007 1
 
3.2%
0.0122 1
 
3.2%
0.0123 1
 
3.2%
0.0125 1
 
3.2%
0.01571 1
 
3.2%
0.01607 1
 
3.2%
ValueCountFrequency (%)
0.9993156096 1
3.2%
0.8665684756 1
3.2%
0.09286 1
3.2%
0.08 1
3.2%
0.075 1
3.2%
0.07071 1
3.2%
0.07 2
6.5%
0.05 1
3.2%
0.04305 1
3.2%
0.03929 2
6.5%

outcome
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
won
30 
lost
 
1

Length

Max length4
Median length3
Mean length3.0322581
Min length3

Characters and Unicode

Total characters94
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st rowwon
2nd rowwon
3rd rowwon
4th rowwon
5th rowwon

Common Values

ValueCountFrequency (%)
won 30
96.8%
lost 1
 
3.2%

Length

2024-05-05T13:16:42.644032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T13:16:42.800530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
won 30
96.8%
lost 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
o 31
33.0%
w 30
31.9%
n 30
31.9%
l 1
 
1.1%
s 1
 
1.1%
t 1
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 31
33.0%
w 30
31.9%
n 30
31.9%
l 1
 
1.1%
s 1
 
1.1%
t 1
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 31
33.0%
w 30
31.9%
n 30
31.9%
l 1
 
1.1%
s 1
 
1.1%
t 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 31
33.0%
w 30
31.9%
n 30
31.9%
l 1
 
1.1%
s 1
 
1.1%
t 1
 
1.1%

pack_strength
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
10mg
31 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters124
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10mg
2nd row10mg
3rd row10mg
4th row10mg
5th row10mg

Common Values

ValueCountFrequency (%)
10mg 31
100.0%

Length

2024-05-05T13:16:42.962049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T13:16:43.125265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
10mg 31
100.0%

Most occurring characters

ValueCountFrequency (%)
1 31
25.0%
0 31
25.0%
m 31
25.0%
g 31
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 124
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 31
25.0%
0 31
25.0%
m 31
25.0%
g 31
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 124
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 31
25.0%
0 31
25.0%
m 31
25.0%
g 31
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 124
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 31
25.0%
0 31
25.0%
m 31
25.0%
g 31
25.0%

participants_no
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
1
16 
2
3
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 16
51.6%
2 7
22.6%
3 5
 
16.1%
4 3
 
9.7%

Length

2024-05-05T13:16:43.275102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T13:16:43.439410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 16
51.6%
2 7
22.6%
3 5
 
16.1%
4 3
 
9.7%

Most occurring characters

ValueCountFrequency (%)
1 16
51.6%
2 7
22.6%
3 5
 
16.1%
4 3
 
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 16
51.6%
2 7
22.6%
3 5
 
16.1%
4 3
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 16
51.6%
2 7
22.6%
3 5
 
16.1%
4 3
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 16
51.6%
2 7
22.6%
3 5
 
16.1%
4 3
 
9.7%

price_participants_16
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)68.2%
Missing9
Missing (%)29.0%
Infinite0
Infinite (%)0.0%
Mean0.026392273
Minimum1 × 10-5
Maximum0.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-05-05T13:16:43.609199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-5
5-th percentile1 × 10-5
Q10.00205
median0.02
Q30.0397875
95-th percentile0.07
Maximum0.08
Range0.07999
Interquartile range (IQR)0.0377375

Descriptive statistics

Standard deviation0.026162796
Coefficient of variation (CV)0.99130516
Kurtosis-0.43044088
Mean0.026392273
Median Absolute Deviation (MAD)0.01975
Skewness0.87848741
Sum0.58063
Variance0.0006844919
MonotonicityNot monotonic
2024-05-05T13:16:43.804921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 × 10-53
 
9.7%
0.07 3
 
9.7%
0.02 3
 
9.7%
0.025 2
 
6.5%
0.0004 1
 
3.2%
0.007 1
 
3.2%
0.014 1
 
3.2%
0.019 1
 
3.2%
0.03 1
 
3.2%
0.0001 1
 
3.2%
Other values (5) 5
16.1%
(Missing) 9
29.0%
ValueCountFrequency (%)
1 × 10-53
9.7%
5 × 10-51
 
3.2%
0.0001 1
 
3.2%
0.0004 1
 
3.2%
0.007 1
 
3.2%
0.014 1
 
3.2%
0.017 1
 
3.2%
0.019 1
 
3.2%
0.02 3
9.7%
0.025 2
6.5%
ValueCountFrequency (%)
0.08 1
 
3.2%
0.07 3
9.7%
0.05 1
 
3.2%
0.04305 1
 
3.2%
0.03 1
 
3.2%
0.025 2
6.5%
0.02 3
9.7%
0.019 1
 
3.2%
0.017 1
 
3.2%
0.014 1
 
3.2%

price_participants_19
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)85.7%
Missing17
Missing (%)54.8%
Infinite0
Infinite (%)0.0%
Mean0.079168463
Minimum0.0122
Maximum0.86656848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-05-05T13:16:43.978971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0122
5-th percentile0.012265
Q10.015
median0.016965
Q30.019875
95-th percentile0.32604897
Maximum0.86656848
Range0.85436848
Interquartile range (IQR)0.004875

Descriptive statistics

Standard deviation0.22674316
Coefficient of variation (CV)2.8640591
Kurtosis13.964806
Mean0.079168463
Median Absolute Deviation (MAD)0.00275
Skewness3.7351411
Sum1.1083585
Variance0.051412459
MonotonicityNot monotonic
2024-05-05T13:16:44.322563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.01786 2
 
6.5%
0.015 2
 
6.5%
0.8665684756 1
 
3.2%
0.01607 1
 
3.2%
0.0125 1
 
3.2%
0.035 1
 
3.2%
0.0205 1
 
3.2%
0.0123 1
 
3.2%
0.018 1
 
3.2%
0.0122 1
 
3.2%
Other values (2) 2
 
6.5%
(Missing) 17
54.8%
ValueCountFrequency (%)
0.0122 1
3.2%
0.0123 1
3.2%
0.0125 1
3.2%
0.015 2
6.5%
0.0155 1
3.2%
0.01607 1
3.2%
0.01786 2
6.5%
0.018 1
3.2%
0.0205 1
3.2%
0.034 1
3.2%
ValueCountFrequency (%)
0.8665684756 1
3.2%
0.035 1
3.2%
0.034 1
3.2%
0.0205 1
3.2%
0.018 1
3.2%
0.01786 2
6.5%
0.01607 1
3.2%
0.0155 1
3.2%
0.015 2
6.5%
0.0125 1
3.2%

price_participants_23
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)83.3%
Missing19
Missing (%)61.3%
Infinite0
Infinite (%)0.0%
Mean0.049135
Minimum0.01571
Maximum0.09286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-05-05T13:16:44.463390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.01571
5-th percentile0.020231
Q10.036165
median0.0414
Q30.0658375
95-th percentile0.0806775
Maximum0.09286
Range0.07715
Interquartile range (IQR)0.0296725

Descriptive statistics

Standard deviation0.02298384
Coefficient of variation (CV)0.46776921
Kurtosis-0.61318865
Mean0.049135
Median Absolute Deviation (MAD)0.02059
Skewness0.36979026
Sum0.58962
Variance0.00052825692
MonotonicityNot monotonic
2024-05-05T13:16:44.638122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.06511 2
 
6.5%
0.03929 2
 
6.5%
0.07071 1
 
3.2%
0.04066 1
 
3.2%
0.04214 1
 
3.2%
0.06802 1
 
3.2%
0.01571 1
 
3.2%
0.02393 1
 
3.2%
0.02679 1
 
3.2%
0.09286 1
 
3.2%
(Missing) 19
61.3%
ValueCountFrequency (%)
0.01571 1
3.2%
0.02393 1
3.2%
0.02679 1
3.2%
0.03929 2
6.5%
0.04066 1
3.2%
0.04214 1
3.2%
0.06511 2
6.5%
0.06802 1
3.2%
0.07071 1
3.2%
0.09286 1
3.2%
ValueCountFrequency (%)
0.09286 1
3.2%
0.07071 1
3.2%
0.06802 1
3.2%
0.06511 2
6.5%
0.04214 1
3.2%
0.04066 1
3.2%
0.03929 2
6.5%
0.02679 1
3.2%
0.02393 1
3.2%
0.01571 1
3.2%

price_participants_24
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing30
Missing (%)96.8%
Memory size1.1 KiB
0.075

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.075

Common Values

ValueCountFrequency (%)
0.075 1
 
3.2%
(Missing) 30
96.8%

Length

2024-05-05T13:16:44.823814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T13:16:44.954126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.075 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
40.0%
. 1
20.0%
7 1
20.0%
5 1
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2
40.0%
. 1
20.0%
7 1
20.0%
5 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2
40.0%
. 1
20.0%
7 1
20.0%
5 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2
40.0%
. 1
20.0%
7 1
20.0%
5 1
20.0%

price_participants_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing30
Missing (%)96.8%
Memory size1.1 KiB
0.0242

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.0242

Common Values

ValueCountFrequency (%)
0.0242 1
 
3.2%
(Missing) 30
96.8%

Length

2024-05-05T13:16:45.082450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T13:16:45.194819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0242 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
33.3%
2 2
33.3%
. 1
16.7%
4 1
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2
33.3%
2 2
33.3%
. 1
16.7%
4 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2
33.3%
2 2
33.3%
. 1
16.7%
4 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2
33.3%
2 2
33.3%
. 1
16.7%
4 1
16.7%

price_participants_5
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing30
Missing (%)96.8%
Memory size1.1 KiB
0.08

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.08

Common Values

ValueCountFrequency (%)
0.08 1
 
3.2%
(Missing) 30
96.8%

Length

2024-05-05T13:16:45.321354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T13:16:45.434684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.08 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
50.0%
. 1
25.0%
8 1
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2
50.0%
. 1
25.0%
8 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2
50.0%
. 1
25.0%
8 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2
50.0%
. 1
25.0%
8 1
25.0%

price_participants_6
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)66.7%
Missing28
Missing (%)90.3%
Memory size1.2 KiB
0.034
0.01699

Length

Max length7
Median length5
Mean length5.6666667
Min length5

Characters and Unicode

Total characters17
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st row0.034
2nd row0.034
3rd row0.01699

Common Values

ValueCountFrequency (%)
0.034 2
 
6.5%
0.01699 1
 
3.2%
(Missing) 28
90.3%

Length

2024-05-05T13:16:45.578409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T13:16:45.719341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.034 2
66.7%
0.01699 1
33.3%

Most occurring characters

ValueCountFrequency (%)
0 6
35.3%
. 3
17.6%
3 2
 
11.8%
4 2
 
11.8%
9 2
 
11.8%
1 1
 
5.9%
6 1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6
35.3%
. 3
17.6%
3 2
 
11.8%
4 2
 
11.8%
9 2
 
11.8%
1 1
 
5.9%
6 1
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6
35.3%
. 3
17.6%
3 2
 
11.8%
4 2
 
11.8%
9 2
 
11.8%
1 1
 
5.9%
6 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6
35.3%
. 3
17.6%
3 2
 
11.8%
4 2
 
11.8%
9 2
 
11.8%
1 1
 
5.9%
6 1
 
5.9%

price_participants_7
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct2
Distinct (%)100.0%
Missing29
Missing (%)93.5%
Memory size1.2 KiB
0.9993156095718888
0.06256

Length

Max length18
Median length12.5
Mean length12.5
Min length7

Characters and Unicode

Total characters25
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.9993156095718888
2nd row0.06256

Common Values

ValueCountFrequency (%)
0.9993156095718888 1
 
3.2%
0.06256 1
 
3.2%
(Missing) 29
93.5%

Length

2024-05-05T13:16:45.855235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T13:16:45.991547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.9993156095718888 1
50.0%
0.06256 1
50.0%

Most occurring characters

ValueCountFrequency (%)
0 4
16.0%
9 4
16.0%
8 4
16.0%
5 3
12.0%
6 3
12.0%
. 2
8.0%
1 2
8.0%
3 1
 
4.0%
7 1
 
4.0%
2 1
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4
16.0%
9 4
16.0%
8 4
16.0%
5 3
12.0%
6 3
12.0%
. 2
8.0%
1 2
8.0%
3 1
 
4.0%
7 1
 
4.0%
2 1
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4
16.0%
9 4
16.0%
8 4
16.0%
5 3
12.0%
6 3
12.0%
. 2
8.0%
1 2
8.0%
3 1
 
4.0%
7 1
 
4.0%
2 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4
16.0%
9 4
16.0%
8 4
16.0%
5 3
12.0%
6 3
12.0%
. 2
8.0%
1 2
8.0%
3 1
 
4.0%
7 1
 
4.0%
2 1
 
4.0%

price_participants_8
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing30
Missing (%)96.8%
Memory size1.1 KiB
0.038

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.038

Common Values

ValueCountFrequency (%)
0.038 1
 
3.2%
(Missing) 30
96.8%

Length

2024-05-05T13:16:46.132990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T13:16:46.255385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.038 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
40.0%
. 1
20.0%
3 1
20.0%
8 1
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2
40.0%
. 1
20.0%
3 1
20.0%
8 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2
40.0%
. 1
20.0%
3 1
20.0%
8 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2
40.0%
. 1
20.0%
3 1
20.0%
8 1
20.0%

published_date
Date

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
Minimum2013-04-29 00:00:00
Maximum2023-10-24 00:00:00
2024-05-05T13:16:46.375575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:46.537886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
Distinct25
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Memory size380.0 B
Minimum2013-04-01 00:00:00
Maximum2023-10-01 00:00:00
2024-05-05T13:16:46.689962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:46.854893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)

quantity_total
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120975.91
Minimum2680.6392
Maximum1179786.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-05-05T13:16:47.019296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2680.6392
5-th percentile4523.9721
Q18929.6623
median32966.511
Q366229.464
95-th percentile660411.6
Maximum1179786.6
Range1177106
Interquartile range (IQR)57299.802

Descriptive statistics

Standard deviation270300.25
Coefficient of variation (CV)2.2343312
Kurtosis11.235017
Mean120975.91
Median Absolute Deviation (MAD)25066.171
Skewness3.4170952
Sum3750253.2
Variance7.3062224 × 1010
MonotonicityNot monotonic
2024-05-05T13:16:47.194616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
32966.51108 1
 
3.2%
66527.92798 1
 
3.2%
5758.720773 1
 
3.2%
30548.84441 1
 
3.2%
6263.290169 1
 
3.2%
56479.37121 1
 
3.2%
160968.9444 1
 
3.2%
95007.35275 1
 
3.2%
1179786.644 1
 
3.2%
36740.26825 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
2680.639219 1
3.2%
3289.22341 1
3.2%
5758.720773 1
3.2%
5900.676494 1
3.2%
6263.290169 1
3.2%
8426.467693 1
3.2%
8589.313285 1
3.2%
8833.156207 1
3.2%
9026.16847 1
3.2%
10766.58581 1
3.2%
ValueCountFrequency (%)
1179786.644 1
3.2%
1018263.113 1
3.2%
302560.0861 1
3.2%
220249.4427 1
3.2%
160968.9444 1
3.2%
109912.3512 1
3.2%
95007.35275 1
3.2%
66527.92798 1
3.2%
65931.00078 1
3.2%
63904.29401 1
3.2%

region
Text

Distinct16
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-05T13:16:47.398538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.3225806
Min length8

Characters and Unicode

Total characters258
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)19.4%

Sample

1st rowregion_1
2nd rowregion_2
3rd rowregion_3
4th rowregion_4
5th rowregion_5
ValueCountFrequency (%)
region_2 3
9.7%
region_5 3
9.7%
region_6 3
9.7%
region_8 3
9.7%
region_11 3
9.7%
region_3 2
 
6.5%
region_4 2
 
6.5%
region_7 2
 
6.5%
region_9 2
 
6.5%
region_12 2
 
6.5%
Other values (6) 6
19.4%
2024-05-05T13:16:47.756053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 31
12.0%
g 31
12.0%
i 31
12.0%
o 31
12.0%
n 31
12.0%
_ 31
12.0%
e 31
12.0%
1 14
5.4%
2 5
 
1.9%
5 4
 
1.6%
Other values (7) 18
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 258
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 31
12.0%
g 31
12.0%
i 31
12.0%
o 31
12.0%
n 31
12.0%
_ 31
12.0%
e 31
12.0%
1 14
5.4%
2 5
 
1.9%
5 4
 
1.6%
Other values (7) 18
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 258
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 31
12.0%
g 31
12.0%
i 31
12.0%
o 31
12.0%
n 31
12.0%
_ 31
12.0%
e 31
12.0%
1 14
5.4%
2 5
 
1.9%
5 4
 
1.6%
Other values (7) 18
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 258
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 31
12.0%
g 31
12.0%
i 31
12.0%
o 31
12.0%
n 31
12.0%
_ 31
12.0%
e 31
12.0%
1 14
5.4%
2 5
 
1.9%
5 4
 
1.6%
Other values (7) 18
7.0%

second_place
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
N/A
16 
participants_23
participants_7
participants_19
participants_6
 
1
Other values (3)

Length

Max length15
Median length3
Mean length8.6451613
Min length3

Characters and Unicode

Total characters268
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)12.9%

Sample

1st rowN/A
2nd rowN/A
3rd rowparticipants_23
4th rowN/A
5th rowN/A

Common Values

ValueCountFrequency (%)
N/A 16
51.6%
participants_23 7
22.6%
participants_7 2
 
6.5%
participants_19 2
 
6.5%
participants_6 1
 
3.2%
participants_4 1
 
3.2%
participants_8 1
 
3.2%
participants_16 1
 
3.2%

Length

2024-05-05T13:16:47.934282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T13:16:48.116576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
n/a 16
51.6%
participants_23 7
22.6%
participants_7 2
 
6.5%
participants_19 2
 
6.5%
participants_6 1
 
3.2%
participants_4 1
 
3.2%
participants_8 1
 
3.2%
participants_16 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
p 30
11.2%
a 30
11.2%
t 30
11.2%
i 30
11.2%
N 16
 
6.0%
A 16
 
6.0%
/ 16
 
6.0%
n 15
 
5.6%
_ 15
 
5.6%
s 15
 
5.6%
Other values (10) 55
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 268
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 30
11.2%
a 30
11.2%
t 30
11.2%
i 30
11.2%
N 16
 
6.0%
A 16
 
6.0%
/ 16
 
6.0%
n 15
 
5.6%
_ 15
 
5.6%
s 15
 
5.6%
Other values (10) 55
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 268
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 30
11.2%
a 30
11.2%
t 30
11.2%
i 30
11.2%
N 16
 
6.0%
A 16
 
6.0%
/ 16
 
6.0%
n 15
 
5.6%
_ 15
 
5.6%
s 15
 
5.6%
Other values (10) 55
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 268
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 30
11.2%
a 30
11.2%
t 30
11.2%
i 30
11.2%
N 16
 
6.0%
A 16
 
6.0%
/ 16
 
6.0%
n 15
 
5.6%
_ 15
 
5.6%
s 15
 
5.6%
Other values (10) 55
20.5%

second_place_outcome
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
N/A
16 
lost
14 
won
 
1

Length

Max length4
Median length3
Mean length3.4516129
Min length3

Characters and Unicode

Total characters107
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st rowN/A
2nd rowN/A
3rd rowlost
4th rowN/A
5th rowN/A

Common Values

ValueCountFrequency (%)
N/A 16
51.6%
lost 14
45.2%
won 1
 
3.2%

Length

2024-05-05T13:16:48.337715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T13:16:48.502506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
n/a 16
51.6%
lost 14
45.2%
won 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
N 16
15.0%
/ 16
15.0%
A 16
15.0%
o 15
14.0%
l 14
13.1%
s 14
13.1%
t 14
13.1%
w 1
 
0.9%
n 1
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 107
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 16
15.0%
/ 16
15.0%
A 16
15.0%
o 15
14.0%
l 14
13.1%
s 14
13.1%
t 14
13.1%
w 1
 
0.9%
n 1
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 107
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 16
15.0%
/ 16
15.0%
A 16
15.0%
o 15
14.0%
l 14
13.1%
s 14
13.1%
t 14
13.1%
w 1
 
0.9%
n 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 107
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 16
15.0%
/ 16
15.0%
A 16
15.0%
o 15
14.0%
l 14
13.1%
s 14
13.1%
t 14
13.1%
w 1
 
0.9%
n 1
 
0.9%

second_place_price
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)86.7%
Missing16
Missing (%)51.6%
Infinite0
Infinite (%)0.0%
Mean0.16331561
Minimum0.01571
Maximum0.99931561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-05-05T13:16:48.654756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.01571
5-th percentile0.021653
Q10.034
median0.04214
Q30.066565
95-th percentile0.90639262
Maximum0.99931561
Range0.98360561
Interquartile range (IQR)0.032565

Descriptive statistics

Standard deviation0.31396596
Coefficient of variation (CV)1.9224492
Kurtosis4.5539134
Mean0.16331561
Median Absolute Deviation (MAD)0.02042
Skewness2.4205926
Sum2.4497341
Variance0.098574624
MonotonicityNot monotonic
2024-05-05T13:16:48.835557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.034 2
 
6.5%
0.06511 2
 
6.5%
0.07071 1
 
3.2%
0.04214 1
 
3.2%
0.06802 1
 
3.2%
0.0242 1
 
3.2%
0.9993156096 1
 
3.2%
0.8665684756 1
 
3.2%
0.038 1
 
3.2%
0.03929 1
 
3.2%
Other values (3) 3
 
9.7%
(Missing) 16
51.6%
ValueCountFrequency (%)
0.01571 1
3.2%
0.0242 1
3.2%
0.025 1
3.2%
0.034 2
6.5%
0.038 1
3.2%
0.03929 1
3.2%
0.04214 1
3.2%
0.06256 1
3.2%
0.06511 2
6.5%
0.06802 1
3.2%
ValueCountFrequency (%)
0.9993156096 1
3.2%
0.8665684756 1
3.2%
0.07071 1
3.2%
0.06802 1
3.2%
0.06511 2
6.5%
0.06256 1
3.2%
0.04214 1
3.2%
0.03929 1
3.2%
0.038 1
3.2%
0.034 2
6.5%

sku
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
molecule_x_10mg_tablet
31 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters682
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmolecule_x_10mg_tablet
2nd rowmolecule_x_10mg_tablet
3rd rowmolecule_x_10mg_tablet
4th rowmolecule_x_10mg_tablet
5th rowmolecule_x_10mg_tablet

Common Values

ValueCountFrequency (%)
molecule_x_10mg_tablet 31
100.0%

Length

2024-05-05T13:16:49.016674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T13:16:49.162764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
molecule_x_10mg_tablet 31
100.0%

Most occurring characters

ValueCountFrequency (%)
l 93
13.6%
e 93
13.6%
_ 93
13.6%
m 62
9.1%
t 62
9.1%
o 31
 
4.5%
c 31
 
4.5%
u 31
 
4.5%
x 31
 
4.5%
1 31
 
4.5%
Other values (4) 124
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 682
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 93
13.6%
e 93
13.6%
_ 93
13.6%
m 62
9.1%
t 62
9.1%
o 31
 
4.5%
c 31
 
4.5%
u 31
 
4.5%
x 31
 
4.5%
1 31
 
4.5%
Other values (4) 124
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 682
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 93
13.6%
e 93
13.6%
_ 93
13.6%
m 62
9.1%
t 62
9.1%
o 31
 
4.5%
c 31
 
4.5%
u 31
 
4.5%
x 31
 
4.5%
1 31
 
4.5%
Other values (4) 124
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 682
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 93
13.6%
e 93
13.6%
_ 93
13.6%
m 62
9.1%
t 62
9.1%
o 31
 
4.5%
c 31
 
4.5%
u 31
 
4.5%
x 31
 
4.5%
1 31
 
4.5%
Other values (4) 124
18.2%
Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size380.0 B
Minimum2013-05-17 00:00:00
Maximum2023-09-26 00:00:00
2024-05-05T13:16:49.328771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:49.529176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

winner
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
participants_16
16 
participants_19
12 
participants_6
participants_23
 
1

Length

Max length15
Median length15
Mean length14.935484
Min length14

Characters and Unicode

Total characters463
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st rowparticipants_16
2nd rowparticipants_16
3rd rowparticipants_16
4th rowparticipants_16
5th rowparticipants_16

Common Values

ValueCountFrequency (%)
participants_16 16
51.6%
participants_19 12
38.7%
participants_6 2
 
6.5%
participants_23 1
 
3.2%

Length

2024-05-05T13:16:49.737979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T13:16:49.905370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
participants_16 16
51.6%
participants_19 12
38.7%
participants_6 2
 
6.5%
participants_23 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
p 62
13.4%
a 62
13.4%
t 62
13.4%
i 62
13.4%
r 31
6.7%
c 31
6.7%
n 31
6.7%
s 31
6.7%
_ 31
6.7%
1 28
6.0%
Other values (4) 32
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 463
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 62
13.4%
a 62
13.4%
t 62
13.4%
i 62
13.4%
r 31
6.7%
c 31
6.7%
n 31
6.7%
s 31
6.7%
_ 31
6.7%
1 28
6.0%
Other values (4) 32
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 463
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 62
13.4%
a 62
13.4%
t 62
13.4%
i 62
13.4%
r 31
6.7%
c 31
6.7%
n 31
6.7%
s 31
6.7%
_ 31
6.7%
1 28
6.0%
Other values (4) 32
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 463
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 62
13.4%
a 62
13.4%
t 62
13.4%
i 62
13.4%
r 31
6.7%
c 31
6.7%
n 31
6.7%
s 31
6.7%
_ 31
6.7%
1 28
6.0%
Other values (4) 32
6.9%

winner_price
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.016299677
Minimum1 × 10-5
Maximum0.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-05-05T13:16:50.081661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-5
5-th percentile1 × 10-5
Q10.01225
median0.01699
Q30.02
95-th percentile0.0345
Maximum0.05
Range0.04999
Interquartile range (IQR)0.00775

Descriptive statistics

Standard deviation0.011421468
Coefficient of variation (CV)0.70071742
Kurtosis1.4072859
Mean0.016299677
Median Absolute Deviation (MAD)0.00449
Skewness0.66374969
Sum0.50529
Variance0.00013044993
MonotonicityNot monotonic
2024-05-05T13:16:50.277650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 × 10-53
 
9.7%
0.02 3
 
9.7%
0.01786 2
 
6.5%
0.015 2
 
6.5%
0.0004 1
 
3.2%
0.02393 1
 
3.2%
0.0122 1
 
3.2%
0.018 1
 
3.2%
0.0123 1
 
3.2%
0.0205 1
 
3.2%
Other values (15) 15
48.4%
ValueCountFrequency (%)
1 × 10-53
9.7%
5 × 10-51
 
3.2%
0.0001 1
 
3.2%
0.0004 1
 
3.2%
0.007 1
 
3.2%
0.0122 1
 
3.2%
0.0123 1
 
3.2%
0.0125 1
 
3.2%
0.014 1
 
3.2%
0.015 2
6.5%
ValueCountFrequency (%)
0.05 1
 
3.2%
0.035 1
 
3.2%
0.034 1
 
3.2%
0.03 1
 
3.2%
0.025 1
 
3.2%
0.02393 1
 
3.2%
0.0205 1
 
3.2%
0.02 3
9.7%
0.019 1
 
3.2%
0.018 1
 
3.2%

duration_total_new
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.387097
Minimum15
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-05-05T13:16:50.448218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile16.5
Q130
median42
Q348.5
95-th percentile64.5
Maximum74
Range59
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation14.48143
Coefficient of variation (CV)0.34990206
Kurtosis-0.16990186
Mean41.387097
Median Absolute Deviation (MAD)7
Skewness-0.00042106842
Sum1283
Variance209.71183
MonotonicityNot monotonic
2024-05-05T13:16:50.612943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
48 6
19.4%
42 4
12.9%
36 3
9.7%
49 2
 
6.5%
15 2
 
6.5%
30 2
 
6.5%
24 2
 
6.5%
62 1
 
3.2%
46 1
 
3.2%
56 1
 
3.2%
Other values (7) 7
22.6%
ValueCountFrequency (%)
15 2
 
6.5%
18 1
 
3.2%
24 2
 
6.5%
26 1
 
3.2%
29 1
 
3.2%
30 2
 
6.5%
36 3
9.7%
42 4
12.9%
46 1
 
3.2%
48 6
19.4%
ValueCountFrequency (%)
74 1
 
3.2%
67 1
 
3.2%
62 1
 
3.2%
56 1
 
3.2%
54 1
 
3.2%
51 1
 
3.2%
49 2
 
6.5%
48 6
19.4%
46 1
 
3.2%
42 4
12.9%

quantity_monthly
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3076.0968
Minimum91
Maximum28285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-05-05T13:16:50.789202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum91
5-th percentile176
Q1272
median1273
Q32254.5
95-th percentile15190
Maximum28285
Range28194
Interquartile range (IQR)1982.5

Descriptive statistics

Standard deviation6349.047
Coefficient of variation (CV)2.0639946
Kurtosis11.693128
Mean3076.0968
Median Absolute Deviation (MAD)1028
Skewness3.483059
Sum95359
Variance40310398
MonotonicityNot monotonic
2024-05-05T13:16:50.979527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
845 1
 
3.2%
2772 1
 
3.2%
240 1
 
3.2%
1273 1
 
3.2%
522 1
 
3.2%
2353 1
 
3.2%
3354 1
 
3.2%
1979 1
 
3.2%
24077 1
 
3.2%
1021 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
91 1
3.2%
164 1
3.2%
188 1
3.2%
223 1
3.2%
234 1
3.2%
239 1
3.2%
240 1
3.2%
245 1
3.2%
299 1
3.2%
522 1
3.2%
ValueCountFrequency (%)
28285 1
3.2%
24077 1
3.2%
6303 1
3.2%
4589 1
3.2%
3354 1
3.2%
3053 1
3.2%
2772 1
3.2%
2353 1
3.2%
2156 1
3.2%
1979 1
3.2%

quantity_yearly
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36913.161
Minimum1092
Maximum339420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-05-05T13:16:51.172657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1092
5-th percentile2112
Q13264
median15276
Q327054
95-th percentile182280
Maximum339420
Range338328
Interquartile range (IQR)23790

Descriptive statistics

Standard deviation76188.564
Coefficient of variation (CV)2.0639946
Kurtosis11.693128
Mean36913.161
Median Absolute Deviation (MAD)12336
Skewness3.483059
Sum1144308
Variance5.8046973 × 109
MonotonicityNot monotonic
2024-05-05T13:16:51.617602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
10140 1
 
3.2%
33264 1
 
3.2%
2880 1
 
3.2%
15276 1
 
3.2%
6264 1
 
3.2%
28236 1
 
3.2%
40248 1
 
3.2%
23748 1
 
3.2%
288924 1
 
3.2%
12252 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
1092 1
3.2%
1968 1
3.2%
2256 1
3.2%
2676 1
3.2%
2808 1
3.2%
2868 1
3.2%
2880 1
3.2%
2940 1
3.2%
3588 1
3.2%
6264 1
3.2%
ValueCountFrequency (%)
339420 1
3.2%
288924 1
3.2%
75636 1
3.2%
55068 1
3.2%
40248 1
3.2%
36636 1
3.2%
33264 1
3.2%
28236 1
3.2%
25872 1
3.2%
23748 1
3.2%

quantity_ext
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140045.23
Minimum3822
Maximum1357680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-05-05T13:16:51.835190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3822
5-th percentile4887
Q112092
median41405
Q388677
95-th percentile750613
Maximum1357680
Range1353858
Interquartile range (IQR)76585

Descriptive statistics

Standard deviation310195.15
Coefficient of variation (CV)2.2149641
Kurtosis11.506011
Mean140045.23
Median Absolute Deviation (MAD)32221
Skewness3.4655929
Sum4341402
Variance9.6221034 × 1010
MonotonicityNot monotonic
2024-05-05T13:16:52.064790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
41405 1
 
3.2%
72072 1
 
3.2%
5760 1
 
3.2%
61104 1
 
3.2%
12528 1
 
3.2%
70590 1
 
3.2%
181116 1
 
3.2%
94992 1
 
3.2%
1179773 1
 
3.2%
36756 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
3822 1
3.2%
4014 1
3.2%
5760 1
3.2%
8424 1
3.2%
8820 1
3.2%
9184 1
3.2%
10038 1
3.2%
11656 1
3.2%
12528 1
3.2%
20033 1
3.2%
ValueCountFrequency (%)
1357680 1
3.2%
1179773 1
3.2%
321453 1
3.2%
220272 1
3.2%
181116 1
3.2%
128226 1
3.2%
94992 1
3.2%
89466 1
3.2%
87888 1
3.2%
74550 1
3.2%

duration_ext
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9677419
Minimum0
Maximum31
Zeros7
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-05-05T13:16:52.248472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median6
Q312
95-th percentile25
Maximum31
Range31
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation8.084899
Coefficient of variation (CV)1.0147039
Kurtosis1.5174712
Mean7.9677419
Median Absolute Deviation (MAD)6
Skewness1.3643223
Sum247
Variance65.365591
MonotonicityNot monotonic
2024-05-05T13:16:52.407481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 8
25.8%
0 7
22.6%
12 4
12.9%
3 3
 
9.7%
10 2
 
6.5%
20 1
 
3.2%
14 1
 
3.2%
31 1
 
3.2%
26 1
 
3.2%
2 1
 
3.2%
Other values (2) 2
 
6.5%
ValueCountFrequency (%)
0 7
22.6%
2 1
 
3.2%
3 3
 
9.7%
5 1
 
3.2%
6 8
25.8%
10 2
 
6.5%
12 4
12.9%
14 1
 
3.2%
20 1
 
3.2%
24 1
 
3.2%
ValueCountFrequency (%)
31 1
 
3.2%
26 1
 
3.2%
24 1
 
3.2%
20 1
 
3.2%
14 1
 
3.2%
12 4
12.9%
10 2
 
6.5%
6 8
25.8%
5 1
 
3.2%
3 3
 
9.7%
Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size380.0 B
Minimum2015-01-01 00:00:00
Maximum2026-09-26 00:00:00
2024-05-05T13:16:52.586915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:52.810426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)

Interactions

2024-05-05T13:16:35.673365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:10.989928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:13.034298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:14.831873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:16.557185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:18.453243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:20.021131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:21.816613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:23.767569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:25.605320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:27.381020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:29.301176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:31.391114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:33.446454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:35.808334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:11.113499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:13.145838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:14.949621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:16.671307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:18.564180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:20.132512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:21.948513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:23.903529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:25.720804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:27.524793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:29.423652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:31.534001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:33.611691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:35.943762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:11.223708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:13.256182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:15.055289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:16.806355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:18.673588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:20.256289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:22.090172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:24.039387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:25.833627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:27.659826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:29.557939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:31.700903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:33.796232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:36.073946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:11.324660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:13.375360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:15.159327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:16.928895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:18.783740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:20.388631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:22.199484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:24.155237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:25.943811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:27.801997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:29.684575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:31.847227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:33.957916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:36.206212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:11.438497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:13.536520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:15.302055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:17.070473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:18.900946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:20.513057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:22.326969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:24.277114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:26.085311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:27.960043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:29.839717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:32.006943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:34.141961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:36.318135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:11.529859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:13.636411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:15.412905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:17.181304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:19.006927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:20.636979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:22.445811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:24.386844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:26.198907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:28.093485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:29.958531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:32.119276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:34.292677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:36.455909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:11.641235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:13.764562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:15.560614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:17.311321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:19.143451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:20.760937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:22.564669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:24.515547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:26.316401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:28.227494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:30.110348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:32.262228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:34.474789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:36.590439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:11.757191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:13.897452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:15.677023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:17.446939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:19.262777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:20.905089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:22.693418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:24.638037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:26.449316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:28.350430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:30.441078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:32.417873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:34.704711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:36.709552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:12.131560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:14.029006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:15.785933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:17.570846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:19.366315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:21.021194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:22.802877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:24.755010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:26.557491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:28.472211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:30.575345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:32.553434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:34.843889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:36.839184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:12.319698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:14.149496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:15.901001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:17.668790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:19.474693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:21.163731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:22.923269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:24.866389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:26.678127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:28.594569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:30.704038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:32.692540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:34.993711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:36.964711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:12.455991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:14.282183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:16.017835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:17.933342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:19.589738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:21.280933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:23.046196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:24.999572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:26.803457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:28.730037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:30.845433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:32.827852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:35.126211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:37.090333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:12.584150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:14.413935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:16.158937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:18.054391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:19.698058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:21.407097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:23.163580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:25.134108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:26.926212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:28.851849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:30.986938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:32.961355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:35.254823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:37.228948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:12.791627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:14.554901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:16.296208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:18.180439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:19.811128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:21.553484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:23.300016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:25.310035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:27.060794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:28.994895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:31.116047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:33.106434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:35.394191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:37.554608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:12.913756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:14.696978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:16.430434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:18.325027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:19.913986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:21.699138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:23.596831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:25.478125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:27.239496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:29.174021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:31.262904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:33.262360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-05T13:16:35.542971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-05T13:16:53.011233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
contract_idcontract_typedurationduration_extduration_total_newmaximum_price_allowedoutcomeparticipants_noprice_participants_16price_participants_19price_participants_23price_participants_6price_participants_7quantity_extquantity_monthlyquantity_totalquantity_yearlysecond_placesecond_place_outcomesecond_place_pricewinnerwinner_price
contract_id1.0000.000-0.095-0.213-0.1680.2490.1070.0000.610-0.357-0.3930.0001.0000.2520.3180.2470.3180.0610.163-0.3760.5790.213
contract_type0.0001.0000.1880.0930.173-0.0670.0000.000-0.1050.2360.1751.0001.000-0.158-0.249-0.224-0.2490.0000.000-0.2270.000-0.171
duration-0.0950.1881.000-0.0160.8300.0600.0760.0340.1780.3590.2711.0001.0000.4580.2150.4900.2150.0000.000-0.0320.0000.298
duration_ext-0.2130.093-0.0161.0000.478-0.0670.8910.000-0.0900.028-0.1980.0001.000-0.035-0.251-0.164-0.2510.2130.599-0.3530.4360.140
duration_total_new-0.1680.1730.8300.4781.000-0.0230.0000.3910.0870.459-0.0780.0001.0000.3830.0820.3440.0820.2390.146-0.2390.0000.349
maximum_price_allowed0.249-0.0670.060-0.067-0.0231.0000.0000.1400.7820.7260.8801.0001.0000.029-0.0200.062-0.0200.5170.0000.8320.0000.731
outcome0.1070.0000.0760.8910.0000.0001.0000.122NaN-0.3100.3941.0001.000-0.0410.0000.0410.0000.5090.9830.2170.965-0.204
participants_no0.0000.0000.0340.0000.3910.1400.1221.0000.7420.3390.5070.0001.000-0.113-0.103-0.085-0.1030.6630.6870.0120.2840.442
price_participants_160.610-0.1050.178-0.0900.0870.782NaN0.7421.000-0.3680.4661.0001.0000.029-0.0150.042-0.0150.3410.586-0.1060.5880.706
price_participants_19-0.3570.2360.3590.0280.4590.726-0.3100.339-0.3681.0000.0850.000NaN0.077-0.0900.117-0.0900.2890.0000.4890.9570.974
price_participants_23-0.3930.1750.271-0.198-0.0780.8800.3940.5070.4660.0851.0000.000NaN-0.165-0.2740.011-0.2740.1760.0000.8870.000-0.229
price_participants_60.0001.0001.0000.0000.0001.0001.0000.0001.0000.0000.0001.0000.0000.8660.8660.8660.8660.0001.000-0.8660.0000.866
price_participants_71.0001.0001.0001.0001.0001.0001.0001.0001.000NaNNaN0.0001.000-1.000-1.000-1.000-1.0001.0001.0001.0001.0001.000
quantity_ext0.252-0.1580.458-0.0350.3830.029-0.041-0.1130.0290.077-0.1650.866-1.0001.0000.9330.9800.9330.0000.000-0.0720.2500.218
quantity_monthly0.318-0.2490.215-0.2510.082-0.0200.000-0.103-0.015-0.090-0.2740.866-1.0000.9331.0000.9391.0000.0000.000-0.0290.2710.088
quantity_total0.247-0.2240.490-0.1640.3440.0620.041-0.0850.0420.1170.0110.866-1.0000.9800.9391.0000.9390.0000.0000.1310.2500.230
quantity_yearly0.318-0.2490.215-0.2510.082-0.0200.000-0.103-0.015-0.090-0.2740.866-1.0000.9331.0000.9391.0000.0000.000-0.0290.2710.088
second_place0.0610.0000.0000.2130.2390.5170.5090.6630.3410.2890.1760.0001.0000.0000.0000.0000.0001.0000.7310.0500.3240.473
second_place_outcome0.1630.0000.0000.5990.1460.0000.9830.6870.5860.0000.0001.0001.0000.0000.0000.0000.0000.7311.000-0.2170.7130.476
second_place_price-0.376-0.227-0.032-0.353-0.2390.8320.2170.012-0.1060.4890.887-0.8661.000-0.072-0.0290.131-0.0290.050-0.2171.0000.000-0.016
winner0.5790.0000.0000.4360.0000.0000.9650.2840.5880.9570.0000.0001.0000.2500.2710.2500.2710.3240.7130.0001.0000.227
winner_price0.213-0.1710.2980.1400.3490.731-0.2040.4420.7060.974-0.2290.8661.0000.2180.0880.2300.0880.4730.476-0.0160.2271.000

Missing values

2024-05-05T13:16:37.816283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-05T13:16:38.569834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

active_ingredientatcbuyercontract_idcontract_typedurationend_date_extensionmaximum_price_allowedoutcomepack_strengthparticipants_noprice_participants_16price_participants_19price_participants_23price_participants_24price_participants_4price_participants_5price_participants_6price_participants_7price_participants_8published_datepublished_date_monthquantity_totalregionsecond_placesecond_place_outcomesecond_place_priceskustart_datewinnerwinner_priceduration_total_newquantity_monthlyquantity_yearlyquantity_extduration_extend_date
0molecule_xC07AB07buyer_11regional392017-06-300.00040won10mg10.00040NaNNaNNaNNaNNaNNaNNaNNaN2013-05-162013-05-0132966.511085region_1N/AN/ANaNmolecule_x_10mg_tablet2013-05-24participants_160.0004049.0845.010140.041405.010.02016-08-24
1molecule_xC07AB07buyer_22wide area482017-06-300.00700won10mg10.00700NaNNaNNaNNaNNaNNaNNaNNaN2013-04-292013-04-01220249.442660region_2N/AN/ANaNmolecule_x_10mg_tablet2013-06-21participants_160.0070048.04589.055068.0220272.00.02017-06-21
2molecule_xC07AB07buyer_33regional122016-02-140.07071won10mg20.01400NaN0.07071NaNNaNNaNNaNNaNNaN2013-06-092013-06-012680.639219region_3participants_23lost0.07071molecule_x_10mg_tablet2014-08-14participants_160.0140018.0223.02676.04014.06.02015-08-14
3molecule_xC07AB07buyer_44regional362017-12-310.02500won10mg10.02500NaNNaNNaNNaNNaNNaNNaNNaN2013-05-042013-05-015900.676494region_4N/AN/ANaNmolecule_x_10mg_tablet2013-05-17participants_160.0250056.0164.01968.09184.020.02016-05-17
4molecule_xC07AB07buyer_55regional362017-06-300.00001won10mg10.00001NaNNaNNaNNaNNaNNaNNaNNaN2013-12-182013-12-013289.223410region_5N/AN/ANaNmolecule_x_10mg_tablet2013-12-17participants_160.0000142.091.01092.03822.06.02016-12-17
5molecule_xC07AB07buyer_66regional482019-04-200.01900won10mg10.01900NaNNaNNaNNaNNaNNaNNaNNaN2013-12-062013-12-019026.168470region_6N/AN/ANaNmolecule_x_10mg_tablet2014-02-21participants_160.0190062.0188.02256.011656.014.02018-02-21
6molecule_xC07AB07buyer_77regional122015-03-310.00001won10mg10.00001NaNNaNNaNNaNNaNNaNNaNNaN2014-02-242014-02-0117282.313832region_7N/AN/ANaNmolecule_x_10mg_tablet2014-01-01participants_160.0000115.01440.017280.021600.03.02015-01-01
7molecule_xC07AB07buyer_88regional362018-03-220.08000won10mg40.03000NaN0.04066NaNNaN0.080.034NaNNaN2014-03-082014-03-0131951.791632region_8participants_6lost0.03400molecule_x_10mg_tablet2014-05-22participants_160.0300046.0888.010656.040848.010.02017-05-22
8molecule_xC07AB07buyer_99regional242016-11-290.00001won10mg10.00001NaNNaNNaNNaNNaNNaNNaNNaN2014-03-182014-03-0135013.918539region_9N/AN/ANaNmolecule_x_10mg_tablet2014-05-30participants_160.0000130.01459.017508.043770.06.02016-05-30
9molecule_xC07AB07buyer_1010regional362020-03-310.00010won10mg10.00010NaNNaNNaNNaNNaNNaNNaNNaN2014-09-072014-09-0110766.585813region_10N/AN/ANaNmolecule_x_10mg_tablet2014-08-31participants_160.0001067.0299.03588.020033.031.02017-08-31
active_ingredientatcbuyercontract_idcontract_typedurationend_date_extensionmaximum_price_allowedoutcomepack_strengthparticipants_noprice_participants_16price_participants_19price_participants_23price_participants_24price_participants_4price_participants_5price_participants_6price_participants_7price_participants_8published_datepublished_date_monthquantity_totalregionsecond_placesecond_place_outcomesecond_place_priceskustart_datewinnerwinner_priceduration_total_newquantity_monthlyquantity_yearlyquantity_extduration_extend_date
21molecule_xC07AB07buyer_1622multi-region242022-09-300.01250won10mg1NaN0.0125NaNNaNNaNNaNNaNNaNNaN2020-06-102020-06-015.175292e+04region_5N/AN/ANaNmolecule_x_10mg_tablet2020-04-29participants_190.0125029.02156.025872.062524.05.02022-04-29
22molecule_xC07AB07buyer_823multi-region362023-07-210.03929won10mg3NaN0.03500.03929NaNNaNNaNNaNNaN0.0382020-06-202020-06-013.674027e+04region_8participants_8lost0.03800molecule_x_10mg_tablet2020-07-22participants_190.0350036.01021.012252.036756.00.02023-07-22
23molecule_xC07AB07buyer_224regional492024-12-250.03929won10mg2NaN0.02050.03929NaNNaNNaNNaNNaNNaN2020-10-112020-10-011.179787e+06region_2participants_23lost0.03929molecule_x_10mg_tablet2020-11-25participants_190.0205049.024077.0288924.01179773.00.02024-12-25
24molecule_xC07AB07buyer_1725regional482025-05-190.01230won10mg1NaN0.0123NaNNaNNaNNaNNaNNaNNaN2021-06-092021-06-019.500735e+04region_14N/AN/ANaNmolecule_x_10mg_tablet2021-05-20participants_190.0123048.01979.023748.094992.00.02025-05-20
25molecule_xC07AB07buyer_1226regional482026-01-050.01800won10mg1NaN0.0180NaNNaNNaNNaNNaNNaNNaN2021-07-092021-07-011.609689e+05region_12N/AN/ANaNmolecule_x_10mg_tablet2021-07-05participants_190.0180054.03354.040248.0181116.06.02025-07-05
26molecule_xC07AB07buyer_1827multi-region242024-09-300.01220won10mg1NaN0.0122NaNNaNNaNNaNNaNNaNNaN2022-03-192022-03-015.647937e+04region_15N/AN/ANaNmolecule_x_10mg_tablet2022-03-31participants_190.0122030.02353.028236.070590.06.02024-03-31
27molecule_xC07AB07buyer_1928regional122024-08-030.01571won10mg2NaN0.01500.01571NaNNaNNaNNaNNaNNaN2022-07-022022-07-016.263290e+03region_6participants_23lost0.01571molecule_x_10mg_tablet2022-08-04participants_190.0150024.0522.06264.012528.012.02023-08-04
28molecule_xC07AB07buyer_1629regional242026-06-160.03400lost10mg2NaN0.03400.02393NaNNaNNaNNaNNaNNaN2022-07-012022-07-013.054884e+04region_16participants_19won0.03400molecule_x_10mg_tablet2022-06-17participants_230.0239348.01273.015276.061104.024.02024-06-17
29molecule_xC07AB07buyer_1930regional242025-07-310.02679won10mg30.0250.01500.02679NaNNaNNaNNaNNaNNaN2023-07-092023-07-015.758721e+03region_6participants_16lost0.02500molecule_x_10mg_tablet2023-08-01participants_190.0150024.0240.02880.05760.00.02025-08-01
30molecule_xC07AB07buyer_831regional362027-03-300.09286won10mg40.0800.01550.09286NaNNaNNaNNaN0.06256NaN2023-10-242023-10-011.099124e+05region_8participants_7lost0.06256molecule_x_10mg_tablet2023-09-26participants_190.0155042.03053.036636.0128226.06.02026-09-26